Skip to main content

Multiobjective evolutionary algorithm IDEA and k-means clustering for modeling multidimenional medical data based on fuzzy cognitive maps

Abstract

The paper concerns the use of evolutionary algorithms to solve the problem of multiobjective optimization and learning of fuzzy cognitive maps (FCMs) on the basis of multidimensional medical data related to diabetes. The analyzed approach consists of two stages. The first stage is to group multidimensional medical data using k-means clustering. The second stage is automatic construction of the FCM model for each group of data based on various criteria depending on the structure and forecasting capabilities. The simulation analysis was performed with the use of the developed multiobjective Individually Directional Evolutionary Algorithm. Experiments show that the collection of fuzzy cognitive maps, in which each element is built on the basis of data for the particular group of patients, allows us to receive higher forecasting accuracy compared to the standard approaches.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  • Amirkhan A, Papageorgiou EI, Mohseni A, Mosavi MR (2017) A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and application. Comput Methods Programs Biomed 142:129–145

    Article  Google Scholar 

  • Bourgani E, Stylios CD, Manis G, Georgopoulos VC (2014) Time dependent fuzzy cognitive maps for medical diagnosis. In: Likas A, Blekas K, Kalles D (eds) Artificial Intelligence: methods and applications. Springer, Cham, pp 753–756

    Google Scholar 

  • Chen SM (1995) Cognitive-map-based decision analysis based on NPN logics. Fuzzy Sets Syst 71(2):153–163

    Article  Google Scholar 

  • Chernorutsky IG (2010) Methods of optimization in control theory. Peter, St. Petersburg ((in Russian))

    Google Scholar 

  • Chi Y, Liu J (2016) Learning of fuzzy cognitive maps with varying densities using a multiobjective evolutionary algorithm. IEEE Trans Fuzzy Syst 24(1):71–81

    Article  Google Scholar 

  • Christoforou A, Andreou AS (2017) A framework for static and dynamic analysis of multilayer fuzzy cognitive maps. Neurocomputing 232:133–145

    Article  Google Scholar 

  • Dickerson JA, Kosko B (1994) Fuzzy virtual worlds as Fuzzy Cognitive Maps. Presence 3:173–189

    Article  Google Scholar 

  • Falcon R, Napoles G, Bello R, Vanhoof K (2019) Granular cognitive maps: a review. Granul Comput 4(3):451–467

    Article  Google Scholar 

  • Fogel DB (2006) Evolutionary computation. Toward a new philosophy of machine intelligence, 3rd edn. Wiley, Hoboken

    MATH  Google Scholar 

  • Homenda W, Jastrzebska A, Pedrycz W (2015) Nodes selection criteria for fuzzy cognitive maps designed to model time series. In: Filev D et al (eds) Intelligent Systems’ 2014. Advances in Intelligent systems and computing 323. Springer, Cham, pp 859–870

  • Kahn M (2019) UCI Machine Learning Repository. http://archive.ics.uci.edu/ml. Washington University, St. Louis, MO, Last accessed 3 Aug

  • Kolahdoozi M, Amirkhani A, Shojaeefard MH, Abraham A (2019) A novel quantum inspired algorithm for sparse fuzzy cognitive maps learning. Appl Intell

  • Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24(1):65–75

    Article  Google Scholar 

  • Kreinovich V, Stylios C (2015) Why Fuzzy Cognitive Maps Are Efficient. International journal of computers communications & control Vol. 10, Issue 5 (October): Special issue on Fuzzy Sets and Applications, pp. 825–833

  • Kubuś Ł (2015) Individually directional evolutionary algorithm for solving global optimization problems-comparative study in international journal of intelligent systems and applications (IJISA) 7(9):12–19

  • Kubuś Ł, Poczeta K, Yastrebov A (2016) A new learning approach for fuzzy cognitive maps based on system performance indicators. 2016 IEEE International Conference on Fuzzy Systems, Vancouver, Canada, pp 1398–1404

  • Kubuś Ł, Yastrebov A, Poczeta K, Poterala M, Gromadzinski L (2018) The use of fuzzy cognitive maps in evaluation of prognosis of chronic heart failure patients. 2018 signal processing: algorithms, architectures, arrangements, and applications, SPA 2018, pp 191–196

  • Lucchiari C, Folgieri R, Pravettoni G (2014) Fuzzy cognitive maps: a tool to improve diagnostic decisions. Diagnosis 1(4):289–293

    Article  Google Scholar 

  • MacQueen JB (1967) Some methods for classification and analysis of multivariate observations, In: Le Cam LM, Neyman J (Eds.), Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1, pp 281–297, California: University of California Press

  • Mateou NH, Andreou AS (2005) Tree-structured multi-layer fuzzy cognitive maps for modelling large scale, complex problems. In: Proceedings – International Conference Comput. Intell. Model. Control Autom. CIMCA 2005 International Conference Intell. Agents, Web Technol. Internet., pp 133–141

  • Papageorgiou EI, Poczeta K (2017) A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks. Neurocomputing 232:113–121

    Article  Google Scholar 

  • Papageorgiou EI, Subramanian J, Karmegam A, Papandrianos N (2015) A risk management model for familial breast cancer: a new application using fuzzy cognitive map method. Comput Methods Programs Biomed 122:123–135

    Article  Google Scholar 

  • Papakostas GA, Koulouriotis DE, Polydoros AS, Tourassis VD (2012) Towards Hebbian learning of fuzzy cognitive maps in pattern classification problems. Expert Syst Appl 39:10620–10629

    Article  Google Scholar 

  • Peng Z, Wu L, Chen Z (2015) NHL and RCGA based multi-relational fuzzy cognitive map modeling for complex systems. Appl Sci 5(4):1399–1411

    Article  Google Scholar 

  • Poczeta K, Kubus L, Yastrebov A (2019) Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts. Biosystems 179:39–47

    Article  Google Scholar 

  • Poczeta K, Kubuś Ł, Yastrebov A (2017) An Evolutionary Algorithm Based on Graph Theory Metrics for Fuzzy Cognitive Maps Learning. In: Martín-Vide C, Neruda R, Vega- Rodríguez M (eds) Theory and Practice of Natural Computing. TPNC 2017. Lecture Notes in Computer Science 10687, Springer, Cham, pp 137–149

  • Rutkowski L (2005) Methods and Techniques of Artificial Intelligence (in Polish). Wydawnictwo Naukowe PWN, Warsaw

    Google Scholar 

  • Salmeron JL, Froelich W (2016) Dynamic optimization of fuzzy cognitive maps for time series forecasting. Knowl-Based Syst 105:29–37

    Article  Google Scholar 

  • Salmeron JL, Papageorgiou EI (2014) Fuzzy grey cognitive maps and nonlinear Hebbian learning in process control. Appl Intell 41:223–234

    Article  Google Scholar 

  • Schaffer J (1985) Multiple Objective Optimization with Vector Evaluated Genetic Algorithms in Proceedings of the First Int. Conference on Genetic Algortihms, pp. 93–100

  • Stach W, Kurgan L, Pedrycz W, Reformat M (2005) Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst 153(3):371–401

    MathSciNet  Article  Google Scholar 

  • Stach W, Pedrycz W, Kurgan LA (2012) Learning of fuzzy cognitive maps using density estimate. IEEE Trans Syst Man Cybern Part B 42(3):900–912

    Article  Google Scholar 

  • Słoń G (2014) Application of Models of Relational Fuzzy Cognitive Maps for Prediction of Work of Complex Systems. LNAI 8467, Springer, pp 307–318

  • Wu K, Liu J (2017) Learning Large-Scale Fuzzy Cognitive Maps Based on Compressed Sensing and Application in Reconstructing Gene Regulatory Networks in IEEE Transactions on Fuzzy Systems 25(6):1546–1560

  • Yastrebov A, Gad S, Słoń S (2008) Bank of artificial neural networks MLP type in symptom systems of technical diagnostics. Pol J Environ Stud 17(2A):118–123

    Google Scholar 

  • Yastrebov A, Kubuś Ł, Poczeta K (2019) An analysis of evolutionary algorithms for multiobjective optimization of structure and learning of fuzzy cognitive maps based on multidimensional medical data. Theory and Practice of Natural Computing 8th International Conference, TPNC 2019, Kingston, Canada, pp 147–158

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Katarzyna Poczeta.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yastrebov, A., Kubuś, Ł. & Poczeta, K. Multiobjective evolutionary algorithm IDEA and k-means clustering for modeling multidimenional medical data based on fuzzy cognitive maps. Nat Comput (2022). https://doi.org/10.1007/s11047-022-09895-1

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11047-022-09895-1

Keywords

  • Fuzzy cognitive maps
  • Multiobjective optimization
  • Evolutionary algorithms
  • K-means clustering
  • Multidimensional medical data